fix flake and mypy

Author: ravinkohli

autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/variance_thresholding/VarianceThreshold.py

@@ -1,8 +1,10 @@
 from typing import Any, Dict, Optional, Union
+
 import numpy as np
+
 from sklearn.feature_selection import VarianceThreshold as SklearnVarianceThreshold
-from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType
 
+from autoPyTorch.datasets.base_dataset import BaseDatasetPropertiesType
 from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.base_tabular_preprocessing import \
     autoPyTorchTabularPreprocessingComponent
 
@@ -31,11 +33,12 @@ def transform(self, X: Dict[str, Any]) -> Dict[str, Any]:
         return X
 
     @staticmethod
-    def get_properties(dataset_properties: Optional[Dict[str, BaseDatasetPropertiesType]] = None
+    def get_properties(
+        dataset_properties: Optional[Dict[str, BaseDatasetPropertiesType]] = None
     ) -> Dict[str, Union[str, bool]]:
 
         return {
             'shortname': 'Variance Threshold',
             'name': 'Variance Threshold (constant feature removal)',
             'handles_sparse': True,
-        }
+        }

test/test_pipeline/components/preprocessing/test_variance_thresholding.py

@@ -5,7 +5,8 @@
 from sklearn.base import BaseEstimator
 from sklearn.compose import make_column_transformer
 
-from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.variance_thresholding.VarianceThreshold import VarianceThreshold
+from autoPyTorch.pipeline.components.preprocessing.tabular_preprocessing.variance_thresholding. \
+    VarianceThreshold import VarianceThreshold
 
 
 def test_variance_threshold():
@@ -38,8 +39,8 @@ def test_variance_threshold():
 
     # make column transformer with returned encoder to fit on data
     column_transformer = make_column_transformer((variance_threshold,
-                                                    X['dataset_properties']['numerical_columns']),
-                                                    remainder='passthrough')
+                                                  X['dataset_properties']['numerical_columns']),
+                                                 remainder='passthrough')
     column_transformer = column_transformer.fit(X['X_train'])
     transformed = column_transformer.transform(data[test_indices])